
import os
import numpy as np
import pandas as pd
import nibabel as nib
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from tqdm.notebook import tqdm

from cerebro import cerebro_brain_utils as cbu
from cerebro import cerebro_brain_viewer as cbv


def ensure_dir(file_name):
    os.makedirs(os.path.dirname(file_name), exist_ok=True)
    return file_name


def write_np(np_obj, file_path):
    with open(file_path, 'wb') as outfile:
        np.save(outfile, np_obj)


def load_np(file_path):
    with open(file_path, 'rb') as infile:
        return np.load(infile)


def plot_cifti_with_cerebro(dscalar_data, axes, colormap=plt.cm.Spectral, clims=(-1,1),):
    my_brain_viewer = cbv.Cerebro_brain_viewer(offscreen=True, background_color=(1,1,1,0),)

    surface = 'inflated'
    surface_model = my_brain_viewer.load_template_GIFTI_cortical_surface_models(surface)

    cifti_space = my_brain_viewer.visualize_cifti_space(
        volumetric_structures='all',
        cifti_expansion_scale=20,
        cifti_left_right_seperation=10,
        volumetric_structure_offset=(0, 5, -40),
        volume_rendering='surface',
    )

    dscalar_layer = my_brain_viewer.add_cifti_dscalar_layer(
        dscalar_data=dscalar_data,
        colormap=colormap,
        clims=clims,
        opacity=0.8)

    ax = axes[0]
    ax.axis('off')
    view = ((-250, 200, 0), None, None, None)
    camconf = my_brain_viewer.view_to_camera_config(view)
    camconf = my_brain_viewer.zoom_camera_to_content(camconf)
    camconf['camera_pos'] = tuple([x * 0.7 for x in camconf['camera_pos']])
    my_brain_viewer.viewer.change_view(**camconf)
    my_brain_viewer.offscreen_draw_to_matplotlib_axes(ax)

    ax = axes[1]
    ax.axis('off')
    camconf = my_brain_viewer.view_to_camera_config("R")
    camconf = my_brain_viewer.zoom_camera_to_content(camconf)
    camconf['camera_pos'] = tuple([x * 0.7 for x in camconf['camera_pos']])
    my_brain_viewer.viewer.change_view(**camconf)
    my_brain_viewer.offscreen_draw_to_matplotlib_axes(ax)

    my_brain_viewer.viewer.window.destroy()
    
    plt.show()


sns.set()
sns.set_style("darkgrid")

%matplotlib inline
%config InlineBackend.figure_format = 'retina'


plt.rc('text', usetex=True)
plt.rc('text.latex', preamble=r'\usepackage{mathtools} \usepackage{sfmath}')

plt.rc('xtick', labelsize=20)
plt.rc('ytick', labelsize=20)
plt.rc('axes', labelsize=24)

plt.rc('figure', dpi=500)


# list of all tasks and the cope number related to each selected contrast
tasks = {
    'EMOTION': '3',  # faces - shapes
    'GAMBLING': '6',  # reward - punish
    'RELATIONAL': '4',  # rel - match
    'SOCIAL': '6',  # tom - random
    'WM': '20',  # face - avg
}


# Compute mean and std, followed by a parametric z-score (one sample t-test)
ground_truth_effect = {}
# Base directory where files are stored at
base_dir='/data/netapp01/work/sina/structural_clustering/PALM_revision_1'

for task in tqdm(tasks, desc="Tasks loop", leave=True):
    ground_truth_effect[task] = load_np(
        '{}/ground_truth/cohen_d_{}_cope{}.dscalar.npy'.format(base_dir, task, tasks[task]),
    )

Tasks loop:   0%|          | 0/5 [00:00<?, ?it/s]


mycm = matplotlib.colors.LinearSegmentedColormap.from_list(
    'my_gradient',
    (
        (0.0, (0.1, 0.1, 1.,)),
        (0.25, (0.1, 1., 1.,)),
        (0.5, (1., 1., 1.,)),
        (0.75, (1., 1., 0.1,)),
        (1.0, (1., 0.1, 0.1,)),
    )
)

fig, ax = plt.subplots(1, 1, figsize=(5, 1),)
ax.imshow(np.linspace(0, 1, 256)[np.newaxis, :].repeat(20,0), cmap=mycm)
ax.set_axis_off()


for task in tasks:
    print(task)
    
    fig, axes = plt.subplots(1, 2, figsize=(10, 8),)
    plt.subplots_adjust(wspace=0, hspace=0)
    plot_cifti_with_cerebro(ground_truth_effect[task], colormap=mycm, axes=axes)

EMOTION

GAMBLING

RELATIONAL

SOCIAL

WM

Topological Cluster Statistics (TCS)¶

Notebook 12: Brain Visualizations, ground truth¶

Packages and basic functions¶

Loading the ground truth¶

Visualizing brains¶